



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__A							       =		'\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

__A							       =		'\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


__A							       =		'\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'



def 			__a	(       lowerCAmelCase_  :   Dict							) ->					List[Any]:






						'''simple docstring'''



						def remove_articles(lowerCAmelCase_  :   Union[str, Any]							):
												UpperCAmelCase_=  re.compile(r"""\b(a|an|the)\b"""     ,re.UNICODE							)
												return re.sub(A_     ,""" """     ,A_							)

						def white_space_fix(lowerCAmelCase_  :   List[Any]							):
												return " ".join(text.split()							)

						def remove_punc(lowerCAmelCase_  :   Optional[Any]							):
												UpperCAmelCase_=  set(string.punctuation							)
												return "".join(ch for ch in text if ch not in exclude							)

						def lower(lowerCAmelCase_  :   Dict							):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(A_							)							)							)							)



def 			__a	(       lowerCAmelCase_  :   List[Any]     ,lowerCAmelCase_  :   Dict							) ->					str:






						'''simple docstring'''



						return int(normalize_answer(A_							) == normalize_answer(A_							)							)



def 			__a	(       lowerCAmelCase_  :   Optional[Any]     ,lowerCAmelCase_  :   List[str]							) ->					Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_=  [any(compute_exact(A_     ,A_							) for ref in refs							) for pred, refs in zip(A_     ,A_							)]
						return (sum(A_							) / len(A_							)) * 1_00



def 			__a	(       lowerCAmelCase_  :   Optional[int]     ,lowerCAmelCase_  :   Dict     ,lowerCAmelCase_  :   List[Any]     ,lowerCAmelCase_  :   Any							) ->					Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=  [rgram for rgrams in rgramslist for rgram in rgrams]
						UpperCAmelCase_=  Counter(A_							)

						UpperCAmelCase_=  Counter(A_							)
						UpperCAmelCase_=  Counter()
						for sgram, scount in sgramcounter.items():
												UpperCAmelCase_=  scount * numref

						UpperCAmelCase_=  Counter(A_							)
						UpperCAmelCase_=  Counter()
						for cgram, ccount in cgramcounter.items():
												UpperCAmelCase_=  ccount * numref

						# KEEP
						UpperCAmelCase_=  sgramcounter_rep & cgramcounter_rep
						UpperCAmelCase_=  keepgramcounter_rep & rgramcounter
						UpperCAmelCase_=  sgramcounter_rep & rgramcounter

						UpperCAmelCase_=  0
						UpperCAmelCase_=  0
						for keepgram in keepgramcountergood_rep:
												keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
												# Fix an alleged bug [2] in the keep score computation.
												# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
												keeptmpscorea += keepgramcountergood_rep[keepgram]
						# Define 0/0=1 instead of 0 to give higher scores for predictions that match
						#      a target exactly.
						UpperCAmelCase_=  1
						UpperCAmelCase_=  1
						if len(A_							) > 0:
												UpperCAmelCase_=  keeptmpscorea / len(A_							)
						if len(A_							) > 0:
												# Fix an alleged bug [2] in the keep score computation.
												# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
												UpperCAmelCase_=  keeptmpscorea / sum(keepgramcounterall_rep.values()							)
						UpperCAmelCase_=  0
						if keepscore_precision > 0 or keepscore_recall > 0:
												UpperCAmelCase_=  2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

						# DELETION
						UpperCAmelCase_=  sgramcounter_rep - cgramcounter_rep
						UpperCAmelCase_=  delgramcounter_rep - rgramcounter
						UpperCAmelCase_=  sgramcounter_rep - rgramcounter
						UpperCAmelCase_=  0
						UpperCAmelCase_=  0
						for delgram in delgramcountergood_rep:
												deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
												deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
						# Define 0/0=1 instead of 0 to give higher scores for predictions that match
						# a target exactly.
						UpperCAmelCase_=  1
						if len(A_							) > 0:
												UpperCAmelCase_=  deltmpscorea / len(A_							)

						# ADDITION
						UpperCAmelCase_=  set(A_							) - set(A_							)
						UpperCAmelCase_=  set(A_							) & set(A_							)
						UpperCAmelCase_=  set(A_							) - set(A_							)

						UpperCAmelCase_=  0
						for addgram in addgramcountergood:
												addtmpscore += 1

						# Define 0/0=1 instead of 0 to give higher scores for predictions that match
						# a target exactly.
						UpperCAmelCase_=  1
						UpperCAmelCase_=  1
						if len(A_							) > 0:
												UpperCAmelCase_=  addtmpscore / len(A_							)
						if len(A_							) > 0:
												UpperCAmelCase_=  addtmpscore / len(A_							)
						UpperCAmelCase_=  0
						if addscore_precision > 0 or addscore_recall > 0:
												UpperCAmelCase_=  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)



def 			__a	(       lowerCAmelCase_  :   List[str]     ,lowerCAmelCase_  :   Dict     ,lowerCAmelCase_  :   Optional[int]							) ->					List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=  len(A_							)

						UpperCAmelCase_=  ssent.split(""" """							)
						UpperCAmelCase_=  csent.split(""" """							)
						UpperCAmelCase_=  []
						UpperCAmelCase_=  []
						UpperCAmelCase_=  []
						UpperCAmelCase_=  []
						UpperCAmelCase_=  []
						UpperCAmelCase_=  []

						UpperCAmelCase_=  []
						UpperCAmelCase_=  []
						UpperCAmelCase_=  []
						UpperCAmelCase_=  []
						for rsent in rsents:
												UpperCAmelCase_=  rsent.split(""" """							)
												UpperCAmelCase_=  []
												UpperCAmelCase_=  []
												UpperCAmelCase_=  []
												ragramslist.append(A_							)
												for i in range(0     ,len(A_							) - 1							):
																		if i < len(A_							) - 1:
																								UpperCAmelCase_=  ragrams[i] + """ """ + ragrams[i + 1]
																								ragrams.append(A_							)
																		if i < len(A_							) - 2:
																								UpperCAmelCase_=  ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
																								ragrams.append(A_							)
																		if i < len(A_							) - 3:
																								UpperCAmelCase_=  ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
																								ragrams.append(A_							)
												ragramslist.append(A_							)
												ragramslist.append(A_							)
												ragramslist.append(A_							)

						for i in range(0     ,len(A_							) - 1							):
												if i < len(A_							) - 1:
																		UpperCAmelCase_=  sagrams[i] + """ """ + sagrams[i + 1]
																		sagrams.append(A_							)
												if i < len(A_							) - 2:
																		UpperCAmelCase_=  sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
																		sagrams.append(A_							)
												if i < len(A_							) - 3:
																		UpperCAmelCase_=  sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
																		sagrams.append(A_							)

						for i in range(0     ,len(A_							) - 1							):
												if i < len(A_							) - 1:
																		UpperCAmelCase_=  cagrams[i] + """ """ + cagrams[i + 1]
																		cagrams.append(A_							)
												if i < len(A_							) - 2:
																		UpperCAmelCase_=  cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
																		cagrams.append(A_							)
												if i < len(A_							) - 3:
																		UpperCAmelCase_=  cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
																		cagrams.append(A_							)

						((UpperCAmelCase_),  (UpperCAmelCase_),  (UpperCAmelCase_))=  SARIngram(A_     ,A_     ,A_     ,A_							)
						((UpperCAmelCase_),  (UpperCAmelCase_),  (UpperCAmelCase_))=  SARIngram(A_     ,A_     ,A_     ,A_							)
						((UpperCAmelCase_),  (UpperCAmelCase_),  (UpperCAmelCase_))=  SARIngram(A_     ,A_     ,A_     ,A_							)
						((UpperCAmelCase_),  (UpperCAmelCase_),  (UpperCAmelCase_))=  SARIngram(A_     ,A_     ,A_     ,A_							)
						UpperCAmelCase_=  sum([keepascore, keepascore, keepascore, keepascore]							) / 4
						UpperCAmelCase_=  sum([delascore, delascore, delascore, delascore]							) / 4
						UpperCAmelCase_=  sum([addascore, addascore, addascore, addascore]							) / 4
						UpperCAmelCase_=  (avgkeepscore + avgdelscore + avgaddscore) / 3
						return finalscore



def 			__a	(       lowerCAmelCase_  :   List[str]     ,lowerCAmelCase_  :   Tuple = True     ,lowerCAmelCase_  :   Union[str, Any] = "13a"     ,lowerCAmelCase_  :   List[str] = True							) ->					List[Any]:






						'''simple docstring'''



						if lowercase:
												UpperCAmelCase_=  sentence.lower()

						if tokenizer in ["13a", "intl"]:
												if version.parse(sacrebleu.__version__							).major >= 2:
																		UpperCAmelCase_=  sacrebleu.metrics.bleu._get_tokenizer(A_							)()(A_							)
												else:
																		UpperCAmelCase_=  sacrebleu.TOKENIZERS[tokenizer]()(A_							)
						elif tokenizer == "moses":
												UpperCAmelCase_=  sacremoses.MosesTokenizer().tokenize(A_     ,return_str=A_     ,escape=A_							)
						elif tokenizer == "penn":
												UpperCAmelCase_=  sacremoses.MosesTokenizer().penn_tokenize(A_     ,return_str=A_							)
						else:
												UpperCAmelCase_=  sentence

						if not return_str:
												UpperCAmelCase_=  normalized_sent.split()

						return normalized_sent



def 			__a	(       lowerCAmelCase_  :   str     ,lowerCAmelCase_  :   List[Any]     ,lowerCAmelCase_  :   int							) ->					Dict:






						'''simple docstring'''



						if not (len(A_							) == len(A_							) == len(A_							)):
												raise ValueError("""Sources length must match predictions and references lengths."""							)
						UpperCAmelCase_=  0
						for src, pred, refs in zip(A_     ,A_     ,A_							):
												sari_score += SARIsent(normalize(A_							)     ,normalize(A_							)     ,[normalize(A_							) for sent in refs]							)
						UpperCAmelCase_=  sari_score / len(A_							)
						return 1_00 * sari_score



def 			__a	(       lowerCAmelCase_  :   Optional[int]     ,lowerCAmelCase_  :   List[Any]     ,lowerCAmelCase_  :   str="exp"     ,lowerCAmelCase_  :   Any=None     ,lowerCAmelCase_  :   Optional[Any]=False     ,lowerCAmelCase_  :   int=False     ,lowerCAmelCase_  :   Any=False     ,) ->					List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=  len(references[0]							)
						if any(len(A_							) != references_per_prediction for refs in references							):
												raise ValueError("""Sacrebleu requires the same number of references for each prediction"""							)
						UpperCAmelCase_=  [[refs[i] for refs in references] for i in range(A_							)]
						UpperCAmelCase_=  sacrebleu.corpus_bleu(
						    A_     ,A_     ,smooth_method=A_     ,smooth_value=A_     ,force=A_     ,lowercase=A_     ,use_effective_order=A_     ,)
						return output.score






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,       _KWARGS_DESCRIPTION)
class 						lowercase		(					datasets.Metric):






	"""simple docstring"""


	def     _SCREAMING_SNAKE_CASE						(      self      :		Dict	)     ->					int:
							return datasets.MetricInfo(
							    description=_DESCRIPTION		,    citation=_CITATION		,    inputs_description=_KWARGS_DESCRIPTION		,    features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""string"""		,    id="""sequence"""	),
							            """references""": datasets.Sequence(datasets.Value("""string"""		,    id="""sequence"""	)		,    id="""references"""	),
							        }	)		,    codebase_urls=[
							        """https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py""",
							        """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
							        """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
							        """https://github.com/mjpost/sacreBLEU""",
							    ]		,    reference_urls=[
							        """https://www.aclweb.org/anthology/Q16-1029.pdf""",
							        """https://github.com/mjpost/sacreBLEU""",
							        """https://en.wikipedia.org/wiki/BLEU""",
							        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
							    ]		,    )




	def     _SCREAMING_SNAKE_CASE						(      self      :		str		,    __UpperCAmelCase      :		Any		,    __UpperCAmelCase      :		Union[str, Any]		,    __UpperCAmelCase      :		str	)     ->					Optional[int]:
							UpperCAmelCase_=  {}
							result.update({"""sari""": compute_sari(sources=UpperCamelCase__		,    predictions=UpperCamelCase__		,    references=UpperCamelCase__	)}	)
							result.update({"""sacrebleu""": compute_sacrebleu(predictions=UpperCamelCase__		,    references=UpperCamelCase__	)}	)
							result.update({"""exact""": compute_em(predictions=UpperCamelCase__		,    references=UpperCamelCase__	)}	)
							return result






import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Optional[Any]=0.01     ,				UpperCamelCase__     :					Tuple=1000       )     ->      str:







    """simple docstring"""






    __magic_name__								=			p_stop
    __magic_name__								=			max_length





  def __iter__(					self     :					Union[str, Any]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			0
    __magic_name__								=			False
    while not stop and count < self.max_length:
      yield count
      count += 1
      __magic_name__								=			random.random() < self.p_stop







class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					str=False     ,				UpperCamelCase__     :					Optional[Any]=True       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			[
        BatchSamplerShard(UpperCamelCase__     ,				2     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )
        for i in range(2       )
    ]
    __magic_name__								=			[list(UpperCamelCase__       ) for batch_sampler_shard in batch_sampler_shards]
    if not split_batches:
      self.assertListEqual([len(UpperCamelCase__       ) for shard in batch_sampler_shards]     ,				[len(UpperCamelCase__       ) for e in expected]       )
    self.assertListEqual(UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is a round multiple of batch size but not total batch size.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1, 0]], [[1, 0, 1]]]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Tuple       )     ->      int:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size or num_processes.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1]], [[0, 1]]]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					int       )     ->      str:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is a round multiple of batch size but not total batch size.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1]], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[int]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size or num_processes.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1]], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
    __magic_name__								=			[BatchSamplerShard(UpperCamelCase__     ,				2     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       ) for i in range(2       )]

    self.assertEqual(len(batch_sampler_shards[0]       )     ,				3       )
    self.assertEqual(len(batch_sampler_shards[1]       )     ,				2       )

    self.assertListEqual(list(batch_sampler_shards[0]       )     ,				[[0, 1, 2], [5, 6, 7, 8], [12, 13]]       )
    self.assertListEqual(list(batch_sampler_shards[1]       )     ,				[[3, 4], [9, 10, 11]]       )
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict=False     ,				UpperCamelCase__     :					Union[str, Any]=2     ,				UpperCamelCase__     :					Union[str, Any]=False       )     ->      Union[str, Any]:







    """simple docstring"""






    random.seed(UpperCamelCase__       )
    __magic_name__								=			list(UpperCamelCase__       )

    __magic_name__								=			[
        IterableDatasetShard(
            UpperCamelCase__     ,				batch_size=UpperCamelCase__     ,				drop_last=UpperCamelCase__     ,				num_processes=UpperCamelCase__     ,				process_index=UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				)
        for i in range(UpperCamelCase__       )
    ]
    __magic_name__								=			[]
    for iterable_dataset_shard in iterable_dataset_shards:
      # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
      random.seed(UpperCamelCase__       )
      iterable_dataset_lists.append(list(UpperCamelCase__       )       )

    __magic_name__								=			batch_size // num_processes if split_batches else batch_size
    # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
    __magic_name__								=			iterable_dataset_lists[0]
    for l in iterable_dataset_lists[1:]:
      self.assertEqual(len(UpperCamelCase__       )     ,				len(UpperCamelCase__       )       )
      self.assertTrue(len(UpperCamelCase__       ) % shard_batch_size == 0       )

    __magic_name__								=			[]
    for idx in range(0     ,				len(UpperCamelCase__       )     ,				UpperCamelCase__       ):
      for l in iterable_dataset_lists:
        observed += l[idx : idx + shard_batch_size]

    if not drop_last:
      while len(UpperCamelCase__       ) < len(UpperCamelCase__       ):
        reference += reference
    self.assertListEqual(UpperCamelCase__     ,				reference[: len(UpperCamelCase__       )]       )
  def 				_lowercase				(					self     :					Tuple       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			42
    __magic_name__								=			RandomIterableDataset()

    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Edge case with a very small dataset
    __magic_name__								=			RandomIterableDataset(max_length=2       )

    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(16       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			SkipBatchSampler(UpperCamelCase__     ,				2       )
    self.assertListEqual(list(UpperCamelCase__       )     ,				[[8, 9, 10, 11], [12, 13, 14, 15]]       )
  def 				_lowercase				(					self     :					Tuple       )     ->      str:







    """simple docstring"""






    __magic_name__								=			SkipDataLoader(list(range(16       )       )     ,				batch_size=4     ,				skip_batches=2       )
    self.assertListEqual([t.tolist() for t in dataloader]     ,				[[8, 9, 10, 11], [12, 13, 14, 15]]       )
  def 				_lowercase				(					self     :					str       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			DataLoader(list(range(16       )       )     ,				batch_size=4       )
    __magic_name__								=			skip_first_batches(UpperCamelCase__     ,				num_batches=2       )
    self.assertListEqual([t.tolist() for t in new_dataloader]     ,				[[8, 9, 10, 11], [12, 13, 14, 15]]       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			DataLoaderShard(list(range(16       )       )     ,				batch_size=4       )
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )

    # Test it also works on the second iteration
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )





  def 				_lowercase				(					self     :					Tuple       )     ->      List[str]:







    """simple docstring"""






    Accelerator()
    __magic_name__								=			DataLoaderDispatcher(range(16       )     ,				batch_size=4       )
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )

    # Test it also works on the second iteration
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )

